The Construction of Roof Greening Intelligent Management System Based on Wireless Sensor Network Technology

Roof greening can make up for the problem of urban green deficiency to some extent, add green space for people in urban life, and also alleviate urban ecological problems and heat island effect. With the development of information technology, the development level of modern automation is higher and higher. Information has penetrated into various industries. So it is very important to develop intelligent management system(IMS) of roof greening to use information technology reasonably. At present, roof greening is mainly artificial operation management, and there are many random factors and can not guarantee that roof greening is always the best state. Irrigation system is also very simple. Therefore, this paper proposes the research and construction of IMS of Roof Greening Based on wireless sensor network(WSN) technology. This paper describes the frame design of IMS of roof greening and the overall design of IMS of roof greening. The design factors of WSN are also studied. The plant survival rate of intelligent management and natural growth is studied by field detection. The effect of roof greening on reducing heat island effect is proved by data chart. The experimental results show that the survival rate of plants under the care of intelligent roof greening system is 91.27, while the natural growth survival rate is only 67.32. At the same time, the green roof plays a great role in the heat island effect. When the sun is the strongest at 12:00 noon, the temperature of the green roof is 25 degrees, while the common roof is 32 degrees.

Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods

Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. This framework is helpful for the quantitative and objective development of roof greening suitability evaluation.